Abhishek Agarwala

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The ability to invert large matrices quickly and accurately determines the effectiveness of a computational tool. Current literature suggests that time complexity of matrix inversion is 2 or higher. This paper redesigns the Gauss Jordan algorithm for matrix inversion on a CUDA platform to exploit the large scale parallelization feature of a massively(More)
• We discuss computational bottlenecks in MD and discuss challenges in parallelizing. • We present a hybrid algorithm using MPI with OpenMP threads for parallelizing MD scheme. • The algorithm is discussed using nano-indentation of Cr films with C indenters using the EAM and Morse potentials. • We study performance of our algorithm for a range of MPI–thread(More)
A parallel algorithm for finding the inverse of the matrix using Gauss Jordan method in OpenMP. The Gauss Jordan method has been chosen for this project because it provides a direct method for obtaining inverse matrix and requires approx. 50% fewer operations unlike other methods. Hence forth it is suitable for massive parallelization. Then, authors have(More)
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